Binary classification from positive-only samples is a variant of PAC learning where the learner receives i.i.d. positive samples and aims to learn a classifier with low error. Previous work by Natarajan, Gereb-Graus, and Shvaytser characterized learnability and revealed a largely negative picture: almost no interesting classes, including two-dimensional halfspaces, are learnable. This poses a challenge for applications from bioinformatics to ecology, where practitioners rely on heuristics. In this work, we initiate a smoothed analysis of positive-only learning. We assume samples from a reference distribution $D$ such that the true distribution $D^*$ is smooth with respect to it. In stark contrast to the worst-case setting, we show that all VC classes become learnable in the smoothed model, requiring $O(VC/ε^2)$ positive samples for $ε$ classification error. We also give an efficient algorithm for any class admitting $\mathrm{poly}(ε)$-approximation by degree-$k$ polynomials whose range is lower-bounded by a constant with respect to $D$ in L1-norm. It runs in time $\mathrm{poly}(d^k/ε)$, qualitatively matching L1-regression. Our results also imply faster or more general algorithms for: (1) estimation with unknown-truncation, giving the first polynomial-time algorithm for estimating exponential-family parameters from samples truncated to an unknown set approximable by non-negative polynomials in L1 norm, improving on [KTZ FOCS19; LMZ FOCS24], who required strong L2-approximation; (2) truncation detection for broad classes, including non-product distributions, improving on [DLNS STOC24]'s who required product distributions; and (3) learning from a list of reference distributions, where samples come from $O(1)$ distributions, one of which witnesses smoothness of $D^*$, as arises when list-decoding algorithms learn samplers for $D^*$ from corrupted data.